##Renewable Energy Dataset

library(readr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(ggthemes)
energy<-read_csv('../../data/IRENA data.csv', skip=1)
## Rows: 67200 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Country/area, Technology, Data Type, Grid connection, Electricity s...
## dbl (1): Year
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
energy_2<-energy%>%
  rename(country='Country/area')

high_gdp<-c("United States","Qatar","Norway","Singapore","United Arab Emirates",
            "Switzerland","Ireland","Luxembourg","Saudi Arabia","Taiwan")
low_gdp<-c("Burundi","Central Africa Republic","Liberia","Democratic Republic of Congo",
           "Mozambique","Niger","Madagascar","Malwai","Chad","Afghanistan")
energy_2$`Electricity statistics`<-
  as.numeric(gsub("-",NA,energy_2$`Electricity statistics`))
world<-energy_2%>%
  group_by(Technology,`Data Type`,`Grid connection`,Year)%>%
  summarise(`Electricity statistics`=sum(`Electricity statistics`,na.rm=TRUE),
            .groups="drop")%>%
  mutate('country'="World")
energy_2<-bind_rows(energy_2,world)

energy_groups<-energy_2%>%
  mutate(group=case_when(
    country=="World"~"World",
    country%in%high_gdp~"High GDP",
    country %in%low_gdp~"Low GDP",
    TRUE~NA_character_))%>%
  filter(!is.na(group))
energy_groups_sdg<-energy_groups%>%
  filter(Year%in% c(2012,2023))
energy_groups_sdg<-energy_groups_sdg%>%
  group_by(group,Year,Technology)%>%
  summarise(total=sum(`Electricity statistics`, na.rm=TRUE),
            .groups="drop")
energy_groups_no_total<-energy_groups_sdg%>%
  filter(!Technology%in%c("Total renewable","Total non-renewable","Total"))

energy_graph<-ggplot(energy_groups_no_total,
       aes(x=group,
           y=total,
           fill=Technology))+
  geom_col(position="fill",width=0.3)+
  scale_y_continuous(labels=scales::percent)+
  facet_wrap(~Year)+
  coord_flip()+
labs(title="Energy Breakdown for Developed and Developing Countries",
     x="Development Status of Countries",
     y="Percent of Energy Type")+
  theme_dark()
ggplotly(energy_graph)

Gapminder Dataset

library(gapminder)
library(dplyr)
library(ggplot2)
data(gapminder)
gapminder%>%head
## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.
gap<-gapminder%>%
  filter(country %in% c('United States', 'China', 'France','Liberia','Ethiopia','Haiti'))%>%
  filter(year>1970) %>% 
  mutate(status = ifelse(country %in% c('United States', 'China', 'France'), 'Developed', 'Developing'))

ggplot(gap,
       aes(x=year, 
           y=lifeExp,
           color=country,
           linetype=status)) +
  geom_line(size=1, alpha=0.5)+
 #scale_linetype_manual(values=c("China"="solid","France"="solid","United States"="solid",
  #                     "Ethiopia"="dashed","Haiti"="dashed","Liberia"="dashed")) +
  labs(title = "Life Expectancy for Developed and Undeveloped Countries Over Time",
  x = "Year",
  y = "Life Expectancy")+
  theme_clean()
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

##Carbon emmissions

library(dplyr)
library(readr)
library(ggplot2)
library(plotly)
library(ggthemes)
url <-'https://nyc3.digitaloceanspaces.com/owid-public/data/co2/owid-co2-data.csv'
carbon <-
  read_csv(url) 
## Rows: 50191 Columns: 79
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): country, iso_code
## dbl (77): year, population, gdp, cement_co2, cement_co2_per_capita, co2, co2...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
carbon<-carbon%>%
  mutate(gdp_per_capita=gdp/population)
high_gdp<-c("United States","Qatar","Norway","Singapore","United Arab Emirates",
            "Switzerland","Ireland","Luxembourg","Saudi Arabia","Taiwan")
low_gdp<-c("Burundi","Central Africa Republic","Liberia","Democratic Republic of Congo",
           "Mozambique","Niger","Madagascar","Malwai","Chad","Afghanistan")
carbon_groups<-carbon%>%
  mutate(group=case_when(
    country=="World"~"World",
    country%in%high_gdp~"High GDP",
    country %in%low_gdp~"Low GDP",
    TRUE~NA_character_))%>%
  filter(!is.na(group))

carbon_wo_world<-carbon%>%
  filter(country!="World")
carbon_groups<-carbon_wo_world%>%
  mutate(group=case_when(
    country%in%high_gdp~"High GDP",
    country%in%low_gdp~"Low GDP",
    TRUE~"World"))
carbon_trend<-carbon_groups%>%
  group_by(year,group)%>%
  summarise(mean_temp=mean(temperature_change_from_ghg, na.rm=TRUE),
            .groups="drop")
plot_1<-ggplot(carbon_trend,
       aes(x=year,y=mean_temp,color=group))+
  geom_line(size=1)+
  geom_vline(xintercept=2012,
             linetype ="dashed",
             size=0.5)+
    scale_x_continuous(limits=c(1850,max(carbon_trend$year)))+
  labs(x="Year",
       y="Temperature Change",
       color="Key")+
  annotate(geom='text',
           x=1985,
           y=.005,
           size=3,
           label='Year when SDGs
     were created')+
  theme_minimal()

 #TEMP CHANGE FROM GHGS
library(dplyr)
library(readr)
library(ggplot2)
library(plotly)
library(ggthemes)
url <-'https://nyc3.digitaloceanspaces.com/owid-public/data/co2/owid-co2-data.csv'
carbon <-
  read_csv(url)
## Rows: 50191 Columns: 79
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): country, iso_code
## dbl (77): year, population, gdp, cement_co2, cement_co2_per_capita, co2, co2...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
carbon_groups<-carbon%>%
  mutate(group=case_when(
    country=="World"~"World",
    country%in%high_gdp~"High GDP",
    country %in%low_gdp~"Low GDP",
    TRUE~NA_character_))%>%
  filter(!is.na(group))

high_gdp<-c("United States","Qatar","Norway","Singapore","United Arab Emirates",
            "Switzerland","Ireland","Luxembourg","Saudi Arabia","Taiwan")
low_gdp<-c("Burundi","Central Africa Republic","Liberia","Democratic Republic of Congo",
           "Mozambique","Niger","Madagascar","Malwai","Chad","Afghanistan")
carbon_wo_world<-carbon%>%
  filter(country!="World")

carbon_groups<-carbon_wo_world%>%
  mutate(group=case_when(
    country%in%high_gdp~"High GDP",
    country%in%low_gdp~"Low GDP",
    TRUE~"World"))
carbon_trend<-carbon_groups%>%
  group_by(year,group)%>%
  summarise(mean_temp=mean(temperature_change_from_ghg, na.rm=TRUE),
            .groups="drop")
plot_2<-ggplot(carbon_trend,
       aes(x=year,y=mean_temp,color=group))+
  geom_line(size=1)+
  geom_vline(xintercept=2012,
             linetype ="dashed",
             size=0.5)+
    scale_x_continuous(limits=c(2000,max(carbon_trend$year)))+
  labs(x="Year",
       y="Temperature Change",
       color="Key")+
  annotate(geom='text',
           x=2008,
           y=0.005,
           size=3,
           label='Year when SDGs
     were created')+
  theme_minimal()
library(ggpubr)
ggarrange(plot_1,plot_2,nrow=1)%>%
  annotate_figure(top=text_grob("Average Temperature Change from Greenhouse Gases from Developed and Developing Countries",size=15))
## Warning: Removed 303 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 750 rows containing missing values or values outside the scale range
## (`geom_line()`).

##Levelized cost of energy

library(readr)
url <- 'https://raw.githubusercontent.com/ericmkeen/sewanee_esus/master/02_energy_sector/levelized-cost-of-energy.csv'
econ <- read_csv(url)
## Rows: 3402 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, source
## dbl (2): year, cost
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
econ%>%head
## # A tibble: 6 × 4
##   country    year source                     cost
##   <chr>     <dbl> <chr>                     <dbl>
## 1 Australia  2010 Bioenergy                NA    
## 2 Australia  2010 Geothermal               NA    
## 3 Australia  2010 Offshore wind            NA    
## 4 Australia  2010 Solar photovoltaic        0.424
## 5 Australia  2010 Concentrated solar power NA    
## 6 Australia  2010 Hydropower               NA
library(ggplot2)
library(dplyr)
library(readr)
library(ggthemes)
url <- 'https://raw.githubusercontent.com/ericmkeen/sewanee_esus/master/02_energy_sector/levelized-cost-of-energy.csv'
econ <- read_csv(url)
## Rows: 3402 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, source
## dbl (2): year, cost
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#cost of renewable and non-renewable sources
econ_filtered<-econ%>%
  filter(country%in% c("United States","France","Sweden","Germany","Japan","China","South Korea","United Kingdom","Netherlands","Denmark"))
            
p<-ggplot(econ_filtered,
       aes(x=year,
           y=cost,
           color=factor(source),
           text=paste("country:",country,
                      "<br>year:",year,
                      "<br>source:",source,
                      "<br>cost:",round(cost,2))))+
  geom_point()+
  geom_vline(xintercept=2012)+
  labs(title="Cost and Use of Energy Types for Developed Countries",
       x="Year",
       y="Cost",
       color="Energy Source")+
  annotate(geom='text',
           x=2003,y=0.52,
           size=3,
           label='Year when SDGs were created')
  
ggplotly(p,tooltip="text")
library(dplyr)
library(knitr)
econ_filtered<-econ%>%
  filter(country%in% c("United States","France","Sweden","Germany","Japan","China","South Korea","United Kingdom","Netherlands","Denmark"))
econ_filtered%>%
  group_by(year,country)%>%
  summarize(cost=mean(cost, na.rm=TRUE))%>%
  kable
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
year country cost
1984 Denmark 0.2371748
1984 Germany 0.2529806
1984 Sweden 0.2523369
1984 United States 0.3238081
1985 Denmark 0.2223528
1985 United States 0.3044088
1986 Denmark 0.2154760
1986 United States 0.2752370
1987 Denmark 0.2056009
1987 United States 0.2694026
1988 Denmark 0.2155145
1988 United States 0.2100088
1989 Denmark 0.1964690
1989 United Kingdom 0.2041509
1989 United States 0.1960202
1990 Denmark 0.1971670
1990 Germany 0.2203359
1990 Sweden 0.2197228
1990 United Kingdom 0.2050081
1990 United States 0.2162832
1991 Denmark 0.1865857
1991 France 0.2035929
1991 Germany 0.2086170
1991 Sweden 0.2080319
1991 United Kingdom 0.1939899
1991 United States 0.2098921
1992 Denmark 0.1832888
1992 Germany 0.2268206
1992 Sweden 0.2261551
1992 United Kingdom 0.2084716
1993 Denmark 0.1841186
1993 France 0.2163642
1993 Germany 0.2220791
1993 Sweden 0.2214135
1993 United Kingdom 0.1912740
1994 Germany 0.1677483
1994 Sweden 0.2090198
1994 United Kingdom 0.1930470
1995 Denmark 0.1532715
1995 Germany 0.2061206
1995 Sweden 0.1985365
1995 United Kingdom 0.1825638
1996 China 0.1816425
1996 Denmark 0.1493380
1996 France 0.1986796
1996 Germany 0.1894378
1996 Sweden 0.2039947
1996 United Kingdom 0.1871813
1997 China 0.1816425
1997 Denmark 0.1534140
1997 France 0.1849604
1997 Germany 0.1882095
1997 Sweden 0.1900097
1997 United Kingdom 0.1740369
1998 China 0.1667260
1998 Denmark 0.1565765
1998 France 0.1830855
1998 Germany 0.1752856
1998 Sweden 0.1881348
1998 United Kingdom 0.1721621
1998 United States 0.1205701
1999 China 0.1632182
1999 Denmark 0.1537264
1999 France 0.1754731
1999 Germany 0.1868320
1999 Sweden 0.1805224
1999 United Kingdom 0.1645496
1999 United States 0.1161334
2000 China 0.1492229
2000 Denmark 0.1673548
2000 France 0.1398779
2000 Germany 0.1841436
2000 Japan 0.1686539
2000 United Kingdom 0.1131557
2000 United States 0.1013885
2001 China 0.1246857
2001 Denmark 0.1613982
2001 France 0.1418427
2001 Germany 0.1922390
2001 Sweden 0.1462334
2001 United Kingdom 0.1283265
2001 United States 0.0953408
2002 China 0.1404714
2002 Denmark 0.1185489
2002 France 0.1469024
2002 Germany 0.1531272
2002 Sweden 0.1673399
2002 United Kingdom 0.1338465
2002 United States 0.0902251
2003 China 0.0986673
2003 Denmark 0.0951942
2003 France 0.1138065
2003 Germany 0.1398898
2003 Sweden 0.1296701
2003 United Kingdom 0.1336529
2003 United States 0.0801150
2004 China 0.1041320
2004 Denmark 0.0849089
2004 France 0.1279492
2004 Germany 0.1399999
2004 Sweden 0.1406168
2004 United Kingdom 0.1402979
2004 United States 0.0882453
2005 China 0.1020668
2005 Denmark 0.0995617
2005 France 0.1264183
2005 Germany 0.1449175
2005 Japan 0.1668268
2005 Sweden 0.1455130
2005 United Kingdom 0.1326896
2005 United States 0.0780249
2006 China 0.0969125
2006 Denmark 0.1232860
2006 France 0.1272836
2006 Germany 0.1491074
2006 Sweden 0.1455130
2006 United Kingdom 0.1331456
2006 United States 0.0881904
2007 China 0.0849595
2007 France 0.1198520
2007 Germany 0.1370723
2007 Sweden 0.1317984
2007 United Kingdom 0.1263473
2007 United States 0.0868338
2008 China 0.0898723
2008 Denmark 0.1187360
2008 France 0.1600386
2008 Germany 0.1644760
2008 Sweden 0.1491739
2008 United Kingdom 0.1387804
2008 United States 0.0988465
2009 China 0.0909917
2009 France 0.1321602
2009 Germany 0.1519318
2009 Sweden 0.1436692
2009 United Kingdom 0.0767546
2009 United States 0.1102728
2010 China 0.1956353
2010 Denmark 0.1196706
2010 France 0.2653962
2010 Germany 0.2593413
2010 Japan 0.1702264
2010 South Korea 0.4509997
2010 Sweden 0.1145101
2010 United Kingdom 0.3181708
2010 United States 0.1616822
2011 China 0.1739857
2011 Denmark 0.1168540
2011 France 0.2606771
2011 Germany 0.2273566
2011 Japan 0.2934478
2011 South Korea 0.4721218
2011 Sweden 0.1090732
2011 United Kingdom 0.2988785
2011 United States 0.1855998
2012 China 0.1374451
2012 Denmark 0.0910542
2012 France 0.2576304
2012 Germany 0.1893619
2012 Japan 0.2495644
2012 South Korea 0.1905774
2012 Sweden 0.1054622
2012 United Kingdom 0.1918472
2012 United States 0.1596206
2013 China 0.1138426
2013 Denmark 0.0889607
2013 France 0.1697370
2013 Germany 0.1585732
2013 Japan 0.2130375
2013 South Korea 0.2252698
2013 Sweden 0.0839654
2013 United Kingdom 0.1639955
2013 United States 0.1511638
2014 China 0.0921410
2014 France 0.1251846
2014 Germany 0.1258181
2014 Japan 0.1928266
2014 South Korea 0.1736466
2014 Sweden 0.0909544
2014 United Kingdom 0.1401094
2014 United States 0.1088508
2015 China 0.0749064
2015 Denmark 0.0633129
2015 France 0.0983155
2015 Germany 0.1055222
2015 Japan 0.1466564
2015 South Korea 0.1596109
2015 Sweden 0.0737552
2015 United Kingdom 0.1150627
2015 United States 0.1001385
2016 China 0.0667813
2016 Denmark 0.0554282
2016 France 0.0828116
2016 Germany 0.0937066
2016 Japan 0.1722541
2016 Netherlands 0.1193991
2016 South Korea 0.1529640
2016 Sweden 0.0631350
2016 United Kingdom 0.1056802
2016 United States 0.0913045
2017 China 0.0559165
2017 Denmark 0.0457878
2017 France 0.0817822
2017 Germany 0.0878803
2017 Japan 0.1285732
2017 Netherlands 0.1328713
2017 South Korea 0.1031784
2017 Sweden 0.0568471
2017 United Kingdom 0.0912089
2017 United States 0.0703899
2018 China 0.0493549
2018 Denmark 0.0437401
2018 France 0.0663755
2018 Germany 0.0796272
2018 Japan 0.1318241
2018 Netherlands 0.1012457
2018 South Korea 0.0850538
2018 Sweden 0.0423804
2018 United Kingdom 0.0822094
2018 United States 0.0580212
2019 China 0.0421479
2019 Denmark 0.0453368
2019 France 0.0578050
2019 Germany 0.0656348
2019 Japan 0.1167038
2019 Netherlands 0.0966343
2019 South Korea 0.0781410
2019 Sweden 0.0405149
2019 United Kingdom 0.0723098
2019 United States 0.0501545
2020 China 0.0372340
2020 Denmark 0.0399788
2020 France 0.0510864
2020 Germany 0.0563428
2020 Japan 0.1057996
2020 Netherlands 0.0912981
2020 South Korea 0.0596249
2020 Sweden 0.0386235
2020 United Kingdom 0.0602736
2020 United States 0.0471003
2021 China 0.0311026
2021 France 0.0464205
2021 Germany 0.0559515
2021 Japan 0.1135182
2021 Netherlands 0.0781061
2021 South Korea 0.0557920
2021 Sweden 0.0363757
2021 United Kingdom 0.0555972
2021 United States 0.0424529